摘要
为了在提高弱小运动目标的检测准确度的同时满足实时性要求,提出了一种基于SVD的改进混合高斯模型弱小运动目标检测算法。针对普通混合高斯模型阈值选取没有针对性的问题,运用基于奇异值分解(SVD)的动态阈值调整更新方法,提高混合高斯模型对弱小运动目标检测准确度。实验结果表明,该算法能够准确、高效地检测特定弱小运动目标。
In order to improve the detection accuracy of dim and small moving targets and meet the real-time requirement, an improved mixture Gauss model based on SVD (singular value decomposition) is proposed for such detection. In view of the problem that there is no pertinence in the threshold selection of ordinary mixture Gauss model, a dynamic threshold adjustment update method based on SVD is used to improve the detection accuracy of the mixture Gauss model for dim and small moving targets. The experimental results show that the algorithm can accurately and efficiently detect specific dim and small moving targets.
作者
王军
李云伟
王恺睿
WANG Jun;LI Yunwei;WANG Kairui(School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China)
出处
《实验技术与管理》
CAS
北大核心
2019年第6期172-175,共4页
Experimental Technology and Management
基金
江苏省教育科学“十二五”规划重点课题(B-b/2015/01/032)
中国矿业大学“十三五”品牌专业建设项目(电子信息工程)(24180017)
中国矿业大学教育教学改革与建设课题(2017YB12)
中国矿业大学研究生教育教学改革研究与实践项目(YJSJG-2018-005)
